International audience

As AI systems take on collaborative roles, they must reason about shared goals and beliefs-not just generate fluent language.The Rational Speech Act (RSA) framework offers a principled approach to pragmatic reasoning, but existing extensions face challenges in scaling to multi-turn, collaborative scenarios.In this paper, we introduce Collaborative Rational Speech Act (CRSA), an information-theoretic (IT) extension of RSA that models multi-turn dialog by optimizing a gain function adapted from rate-distortion theory.This gain is an extension of the gain model that is maximized in the original RSA model but takes into account the scenario in which both agents in a conversation have private information and produce utterances conditioned on the dialog.We demonstrate the effectiveness of CRSA on referential games and template-based doctor-patient dialogs in the medical domain.Empirical results show that CRSA yields more consistent, interpretable, and collaborative behavior than existing baselines, paving the way for more pragmatically competent language agents.

Information extraction from text is essential in data science and artificial intelligence, especially with the increase in the number of scientific articles. Robust methods are needed to structure texts and highlight key information. Named Entity Recognition (NER) identifies and classifies major elements in text, aiding in dataset structuring and tagging.Under the FAIRClinical and ShareFAIR projects, we aimed to extract information from clinical trial publications using NER models. These models have been successfully used for clinical trial information extraction. In this paper, we report on the difficulties met in reusing existing NER solutions, focusing on mandatory replicability. This paper discusses the challenges faced in replicating a notable study, the difficulties encountered and lessons learned. It compares our experiment with the feedback provided by the literature and draws conclusions.

Henning Wachsmuth, Nona Naderi, Yufang Hou, Yonatan Bilu, Vinodkumar Prabhakaran, Tim Alberdingk Thijm, Graeme Hirst, Benno Stein. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. 2017.

MOTIVATION: Semantic tagging of organism mentions in full-text articles is an important part of literature mining and semantic enrichment solutions. Tagged organism mentions also play a pivotal role in disambiguating other entities in a text, such as proteins. A high-precision organism tagging system must be able to detect the numerous forms of organism mentions, including common names as well as the traditional taxonomic groups: genus, species and strains. In addition, such a system must resolve abbreviations and acronyms, assign the scientific name and if possible link the detected mention to the NCBI Taxonomy database for further semantic queries and literature navigation. RESULTS: We present the OrganismTagger, a hybrid rule-based/machine learning system to extract organism mentions from the literature. It includes tools for automatically generating lexical and ontological resources from a copy of the NCBI Taxonomy database, thereby facilitating system updates by end users. Its novel ontology-based resources can also be reused in other semantic mining and linked data tasks. Each detected organism mention is normalized to a canonical name through the resolution of acronyms and abbreviations and subsequently grounded with an NCBI Taxonomy database ID. In particular, our system combines a novel machine-learning approach with rule-based and lexical methods for detecting strain mentions in documents. On our manually annotated OT corpus, the OrganismTagger achieves a precision of 95%, a recall of 94% and a grounding accuracy of 97.5%. On the manually annotated corpus of Linnaeus-100, the results show a precision of 99%, recall of 97% and grounding accuracy of 97.4%. AVAILABILITY: The OrganismTagger, including supporting tools, resources, training data and manual annotations, as well as end user and developer documentation, is freely available under an open-source license at http://www.semanticsoftware.info/organism-tagger. CONTACT: [email protected].